Significant amounts of research and development have been performed with respect to gesture recognition using video and image processing, and more recently, depth sensor data. For example, Microsoft Corporation's Kinect™ device provides a skeletal tracking system, allowing the design of games with body gestures as an interaction mode.
In contrast to skeletal tracking/body gesture recognition, hand gestures are more difficult to recognize because hand motions are more subtle and there are considerable occlusions between the fingers. However, hand gestures convey significant information and are commonly used for human to human communication. This is likely because hand gestures feel natural to humans, and indeed, hand gestures are often used instinctively and subconsciously.
Known attempts at recognizing hand gestures have not been particularly successful. For example, one depth-based gesture recognition system was limited to recognizing static gestures, in which the user had to wear a black wrist band in order to facilitate clear hand segmentation. Another recognition system was dynamic, but was very limited, as only a few gestures were able to be recognized by the system's rule-based classification of shapes and trajectories of the moving hand; further the system was not able to adapt to different hand orientations.